#!/usr/bin/env python # ------------------------------------------------------------------------------------------------------% # Created by "Thieu Nguyen" at 19:05, 29/05/2020 % # % # Email: [email protected] % # Homepage: https://www.researchgate.net/profile/Thieu_Nguyen6 % # Github: https://github.com/thieunguyen5991 % #-------------------------------------------------------------------------------------------------------% from opfunu.cec_basic.cec2014_nobias import * from mealpy.swarm_based.SpaSA import BaseSpaSA ## Setting parameters objective_func = F21 problem_size = 3000 domain_range = [-150, 150] log = True epoch = 100 pop_size = 50 md1 = BaseSpaSA(objective_func, problem_size, domain_range, log, epoch, pop_size) best_pos1, best_fit1, list_loss1 = md1._train__() print(best_fit1) print("========================================================")
from mealpy.swarm_based.GWO import BaseGWO
from mealpy.human_based.TLO import BaseTLO
from mealpy.human_based.QSA import ImprovedQSA
from mealpy.physics_based.EFO import BaseEFO
temp1 = BaseTLO(func,
problem_size=problem_size,
domain_range=(-100, 100),
log=True,
epoch=epoch,
pop_size=50)
temp1._train__()
temp1 = BaseSpaSA(func,
problem_size=problem_size,
domain_range=(-100, 100),
log=True,
epoch=epoch,
pop_size=50)
temp1._train__()
temp2 = BaseEFO(func,
problem_size=problem_size,
domain_range=(-100, 100),
log=True,
epoch=epoch,
pop_size=50)
temp2._train__()
temp2 = ImprovedQSA(func,
problem_size=problem_size,
domain_range=(-100, 100),
#-------------------------------------------------------------------------------------------------------%
from opfunu.cec_basic.cec2014_nobias import *
from mealpy.swarm_based.SpaSA import BaseSpaSA, OriginalSpaSA
## Setting parameters
obj_func = F19
# lb = [-15, -10, -3, -15, -10, -3, -15, -10, -3, -15, -10, -3, -15, -10, -3]
# ub = [15, 10, 3, 15, 10, 3, 15, 10, 3, 15, 10, 3, 15, 10, 3]
lb = [-100]
ub = [100]
problem_size = 1000
batch_size = 25
verbose = True
epoch = 100
pop_size = 50
md1 = OriginalSpaSA(obj_func, lb, ub, problem_size, batch_size, verbose, epoch,
pop_size)
best_pos1, best_fit1, list_loss1 = md1.train()
print(md1.solution[0])
print(md1.solution[1])
print(md1.loss_train)
md1 = BaseSpaSA(obj_func, lb, ub, problem_size, batch_size, verbose, epoch,
pop_size)
best_pos1, best_fit1, list_loss1 = md1.train()
print(md1.solution[0])
print(md1.solution[1])
print(md1.loss_train)
list_funcs = [
F30,
]
problem_size = 10
solution = np.ones(problem_size)
for F in list_funcs:
result1 = F(solution)
print(result1)
# test1 = F14(solution, shift_num=1, rotate_num=1)
# print(test1)
temp = BaseSpaSA(F23,
problem_size=10,
domain_range=(-100, 100),
log=True,
epoch=1000,
pop_size=100)
temp._train__()
temp = BaseWOA(F23,
problem_size=10,
domain_range=(-100, 100),
log=True,
epoch=1000,
pop_size=100)
temp._train__()
from opfunu.cec_basic.cec2014_nobias import *
from mealpy.swarm_based.SpaSA import BaseSpaSA, OriginalSpaSA
# Setting parameters
obj_func = F5
verbose = False
epoch = 500
pop_size = 50
# A - Different way to provide lower bound and upper bound. Here are some examples:
## 1. When you have different lower bound and upper bound for each parameters
lb1 = [-3, -5, 1]
ub1 = [5, 10, 100]
md1 = BaseSpaSA(obj_func, lb1, ub1, verbose, epoch, pop_size)
best_pos1, best_fit1, list_loss1 = md1.train()
print(md1.solution[1])
## 2. When you have same lower bound and upper bound for each parameters, then you can use:
## + int or float: then you need to specify your problem size (number of dimensions)
problemSize = 10
lb2 = -5
ub2 = 10
md2 = BaseSpaSA(
obj_func, lb2, ub2, verbose, epoch, pop_size,
problem_size=problemSize) # Remember the keyword "problem_size"
best_pos1, best_fit1, list_loss1 = md2.train()
print(md2.solution[1])
## + array: 2 ways